Recent developments in monitoring and modelling airborne pollen, a review

References

• Aboulaich N, Achmakh L, Bouziane H, Trigo MM, Recio M, Kadiri M, Cabezudo B, Riadi H, Kazzaz M. 2013. Effect of meteorological parameters on Poaceae pollen in the atmosphere of Tetouan (NW Morocco). International Journal of Biometeorology 57(2): 197‒205. doi:10.1007/s00484-012-0566-2.
   PubMed Web of Science ®Google Scholar
• Achmakh L, Bouziane H, Aboulaich N, Trigo MM, Janati A, Kadiri M. 2015. Airborne pollen of Olea europaea L. in Tetouan (NW Morocco): Heat requirements and forecasts. Aerobiologia 31(2): 191‒199. doi:10.1007/s10453-014-9356-0.
   Web of Science ®Google Scholar
• Aguilera F, Fornaciari M, Ruiz-Valenzuela L, Galán C, Msallem M, Dhiab A, la Guardia C-D, del Mar Trigo M, Bonofiglio T, Orlandi F. 2015a. Phenological models to predict the main flowering phases of olive (Olea europaea L.) along a latitudinal and longitudinal gradient across the mediterranean region. International Journal of Biometeorology 59(5): 629‒641. doi:10.1007/s00484-014-0876-7.
   PubMed Web of Science ®Google Scholar
• Aguilera F, Orlandi F, Ruiz-Valenzuela L, Msallem M, Fornaciari M. 2015b. Analysis and interpretation of long temporal trends in cumulative temperatures and olive reproductive features using a seasonal trend decomposition procedure. Agricultural and Forest Meteorology 203: 208‒216. doi:10.1016/j.agrformet.2014.11.019.
   Web of Science ®Google Scholar
• Albertini R, Ugolotti M, Buters J, Weber B, Thibaudon M, Smith M, Galan C, Brandao R, Antunes C, Grewling L, Rantio-Lehtimäki A, Sofiev M, Jäger S, Berger U, Sauliene I, Cecchi L. 2013. The European project HIALINE (health impacts of airborne allergen information network): Results of pollen and allergen of Betula monitoring in Parma (2009). Review of Allergy and Clinical Immunology 23(1): 14‒20.
   Google Scholar
• Arca B, Pellizzaro G, Canu A, Vargiu A. 2004. Use of neural networks to short-term forecast of airborne pollen data. 16th biometeorology and aerobiology (16BIOAERO). Vancouver: American Meteorological Society.
   Google Scholar
• Astray G, Fernández-González M, Rodríguez-Rajo FJ, López D, Mejuto JC. 2016. Airborne castanea pollen forecasting model for ecological and allergological implementation. Science of the Total Environment 548–549: 110‒121. doi:10.1016/j.scitotenv.2015.12.126.
   PubMed Web of Science ®Google Scholar
• Aznarte MJL, Benítez Sánchez JM, Lugilde DN, de Linares Fernández C, de la Guardia CD, Sánchez FA. 2007. Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models. Expert Systems with Applications 32: 1218‒1225.
   Web of Science ®Google Scholar
• Balram D, Lian K-Y, Sebastian N. 2019. Air quality warning system based on a localized PM2.5 soft sensor using a novel approach of Bayesian regularized neural network via forward feature selection. Ecotoxicology and Environmental Safety 182: 109386. doi:10.1016/j.ecoenv.2019.109386.
   PubMed Web of Science ®Google Scholar
• Beggs PJ, Šikoparija B, Smith M. 2017. Aerobiology in the international journal of biometeorology, 1957–2017. International Journal of Biometeorology 61(S1): 51‒58. doi:10.1007/s00484-017-1374-5.
   PubMed Web of Science ®Google Scholar
• Bilińska D, Kryza M, Werner M, Malkiewicz M. 2019. The variability of pollen concentrations at two stations in the city of Wrocław in Poland. Aerobiologia 35: 421‒439.
   Web of Science ®Google Scholar
• Bilińska D, Skjøth CA, Werner M, Kryza M, Malkiewicz M, Krynicka J, Drzeniecka-Osiadacz A. 2017. Source regions of ragweed pollen arriving in south-western Poland and the influence of meteorological data on the HYSPLIT model results. Aerobiologia 33(3): 315‒326. doi:10.1007/s10453-017-9471-9.
   PubMed Web of Science ®Google Scholar
• Bogawski P, Borycka K, Grewling Ł, Kasprzyk I. 2019a. Detecting distant sources of airborne pollen for Poland: Integrating back-trajectory and dispersion modelling with a satellite-based phenology. Science of the Total Environment 689: 109‒125. doi:10.1016/j.scitotenv.2019.06.348.
   PubMed Web of Science ®Google Scholar
• Bogawski P, Grewling Ł, Jackowiak B. 2019b. Predicting the onset of Betula pendula flowering in Poznań (Poland) using remote sensing thermal data. Science of the Total Environment 658: 1485‒1499. doi:10.1016/j.scitotenv.2018.12.295.
   PubMed Web of Science ®Google Scholar
• Bonini M, Šikoparija B, Prentović M, Cislaghi G, Colombo P, Testoni C, Grewling L, Lommen STE, Müller-Schärer H, Smith M. 2015. Is the recent decrease in airborne Ambrosia pollen in the Milan area due to the accidental introduction of the ragweed leaf beetle Ophraella communa? Aerobiologia 31(4): 499‒513. doi:10.1007/s10453-015-9380-8.
   Web of Science ®Google Scholar
• Bonini M, Šikoparija B, Skjøth CA, Cislaghi G, Colombo P, Testoni C, Smith M. 2018. Ambrosia pollen source inventory for Italy: A multi-purpose tool to assess the impact of the ragweed leaf beetle (Ophraella communa LeSage) on populations of its host plant. International Journal of Biometeorology 62(4): 597‒608. doi:10.1007/s00484-017-1469-z.
   PubMed Web of Science ®Google Scholar
• Brennan GL, Potter C, De Vere N, Griffith GW, Skjøth CA, Osborne NJ, Wheeler BW, McInnes RN, Clewlow Y, Barber A. 2019. Temperate airborne grass pollen defined by spatio-temporal shifts in community composition. Nature Ecology & Evolution 3(5): 750‒754. doi:10.1038/s41559-019-0849-7.
   PubMed Web of Science ®Google Scholar
• Brighetti MA, Costa C, Menesatti P, Antonucci F, Tripodi S, Travaglini A. 2014. Multivariate statistical forecasting modeling to predict Poaceae pollen critical concentrations by meteoclimatic data. Aerobiologia 30(1): 25‒33. doi:10.1007/s10453-013-9305-3.
   Web of Science ®Google Scholar
• Burki C, Šikoparija B, Thibaudon M, Oliver G, Magyar D, Udvardy O, Leelőssy Á, Charpilloz C, Pauling A. 2019. Artificial neural networks can be used for Ambrosia pollen emission parameterization in COSMO-ART. Atmospheric Environment 218: 116969. doi:10.1016/j.atmosenv.2019.116969.
   Web of Science ®Google Scholar
• Buters JTM, Antunes CM, Galveias A, Bergmann KC, Thibaudon M, Galán C, Schmidt-Weber C, Oteros J. 2018. Pollen and spore monitoring in the world. Clinical and Translational Allergy 8(1): 89. doi:10.1186/s13601-018-0197-8.
   Web of Science ®Google Scholar
• Buters JTM, Thibaudon M, Smith M, Kennedy R, Rantio-Lehtimaki A, Albertini R, Reese G, Weber B, Galan C, Brandao R, Antunes CM, Jaeger S, Berger U, Celenk S, Grewling L, Jackowiak B, Sauliene I, Weichenmeier I, Pusch G, Sarioglu H, Ueffing M, Behrendt H, Prank M, Sofiev M, Cecchi L, HIALINE-Working-Group. 2012. Release of Bet v 1 from birch pollen from 5 European countries. Results from the HIALINE Study. Atmospheric Environment 55: 496‒505.
   Web of Science ®Google Scholar
• Calvo A, Baumgardner D, Castro A, Fernández-González D, Vega-Maray A, Valencia-Barrera R, Oduber F, Blanco-Alegre C, Fraile R. 2018. Daily behavior of urban fluorescing aerosol particles in northwest Spain daily behavior of urban fluorescing aerosol particles in northwest Spain. Atmospheric Environment 184: 262‒277. doi:10.1016/j.atmosenv.2018.04.027.
   Web of Science ®Google Scholar
• Chappuis C, Tummon F, Clot B, Konzelmann T, Calpini B, Crouzy B. 2020. Automatic pollen monitoring: First insights from hourly data. Aerobiologia 36(2): 159‒170. doi:10.1007/s10453-019-09619-6.
   Web of Science ®Google Scholar
• Charalampopoulos A, Lazarina M, Tsiripidis I, Vokou D. 2018. Quantifying the relationship between airborne pollen and vegetation in the urban environment. Aerobiologia 34(3): 285‒300. doi:10.1007/s10453-018-9513-y.
   Web of Science ®Google Scholar
• Comtois P. 1998. Statistical analysis of aerobiological data. In: Mandrioli P, Comtois P, Levizzani V, eds. Methods in aerobiology, 262. Bologna: Pitagora Editrice.
   Google Scholar
• Comtois P, Alcazar P, Neron D. 1999. Pollen counts statistics and its relevance to precision. Aerobiologia 15: 19‒28.
   Google Scholar
• Crouzy B, Stella M, Konzelmann T, Calpini B, Clot B. 2016. All-optical automatic pollen identification: Towards an operational system. Atmospheric Environment 140: 202‒212. doi:10.1016/j.atmosenv.2016.05.062.
   Web of Science ®Google Scholar
• Csépe Z, Leelőssy Á, Mányoki G, Kajtor-Apatini D, Udvardy O, Péter B, Páldy A, Gelybó G, Szigeti T, Pándics T, Kofol-Seliger A, Simčič A, Leru PM, Eftimie AM, Šikoparija B, Radišić P, Stjepanović B, Hrga I, Večenaj A, Vucić A, Peroš-Pucar D, Škorić T, Ščevková J, Bastl M, Berger U, Magyar D. 2020. The application of a neural network-based ragweed pollen forecast by the ragweed pollen alarm system in the Pannonian biogeographical region. Aerobiologia 36(2): 131‒140. doi:10.1007/s10453-019-09615-w.
   Web of Science ®Google Scholar
• Csépe Z, Makra L, Voukantsis D, Matyasovszky I, Tusnády G, Karatzas K, Thibaudon M. 2014. Predicting daily ragweed pollen concentrations using computational intelligence techniques over two heavily polluted areas in Europe. Science of the Total Environment 476–477: 542‒552.
   PubMed Web of Science ®Google Scholar
• Dahl A, Galán C, Hajkova L, Pauling A, Sikoparija B, Smith M, Vokou D. 2013. The onset, course and intensity of the pollen season. In: Sofiev M, Bergmann K-C, eds. Allergenic pollen. A review of the production, release, distribution and health impacts, 29–70. Dordrecht: Springer Science+Business Media.
   Google Scholar
• Dai W, Jin H, Zhang Y, Liu T, Zhou Z. 2019. Detecting temporal changes in the temperature sensitivity of spring phenology with global warming: Application of machine learning in phenological model. Agricultural and Forest Meteorology 279: 107702. doi:10.1016/j.agrformet.2019.107702.
   Web of Science ®Google Scholar
• de Weger LA, Beerthuizen T, Hiemstra PS, Sont JK. 2014. Development and validation of a 5-day-ahead hay fever forecast for patients with grass-pollen-induced allergic rhinitis. International Journal of Biometeorology 58(7): 1047‒1055. doi:10.1007/s00484-013-0753-9.
   PubMed Web of Science ®Google Scholar
• de Weger LA, Bergmann K-C, Rantio-Lehtimäki A, Dahl Å, Buters J, Déchamp C, Belmonte J, Thibaudon M, Cecchi L, Besancenot JP, Galán C, Waisel Y. 2013. Impact of Pollen. In: Sofiev M, Bergmann KC, eds. Allergenic pollen, 161‒216. Dordrecht: Springer Science+Business Media.
   Google Scholar
• Donders TH, Hagemans K, Dekker SC, de Weger LA, De Klerk P, Wagner-Cremer F. 2014. Region-specific sensitivity of anemophilous pollen deposition to temperature and precipitation. PLoS ONE 9(8): e104774. doi:10.1371/journal.pone.0104774.
   PubMed Web of Science ®Google Scholar
• Du J, Liu Y, Yu Y, Yan W. 2017. A prediction of precipitation data based on support vector machine and particle swarm optimization (PSO-SVM) algorithms. Algorithms 10(2): 57. doi:10.3390/a10020057.
   Web of Science ®Google Scholar
• Efstathiou C, Isukapalli S, Georgopoulos P. 2011. A mechanistic modeling system for estimating large-scale emissions and transport of pollen and co-allergens. Atmospheric Environment 45(13): 2260‒2276. doi:10.1016/j.atmosenv.2010.12.008.
   Web of Science ®Google Scholar
• Escabias M, Valderrama MJ, Aguilera AM, Santofimia ME, Aguilera-Morillo MC. 2013. Stepwise selection of functional covariates in forecasting peak levels of olive pollen. Stochastic Environmental Research and Risk Assessment 27(2): 367‒376. doi:10.1007/s00477-012-0655-0.
   Web of Science ®Google Scholar
• Estrella N, Menzel A, Krämer U, Behrendt H. 2006. Integration of flowering dates in phenology and pollen counts in aerobiology: Analysis of their spatial and temporal coherence in Germany (1992‒1999). International Journal of Biometeorology 51(1): 49‒59. doi:10.1007/s00484-006-0038-7.
   PubMed Web of Science ®Google Scholar
• Feeney P, Rodríguez SF, Molina R, McGillicuddy E, Hellebust S, Quirke M, Daly S, O’Connor D, Sodeau J. 2018. A comparison of on-line and off-line bioaerosol measurements at a biowaste site. Waste Management 76: 323‒338. doi:10.1016/j.wasman.2018.02.035.
   PubMed Web of Science ®Google Scholar
• Fennelly MJ, Sewell G, Prentice MB, O’Connor DJ, Sodeau JR. 2018. The use of real-time fluorescence instrumentation to monitor ambient primary biological aerosol particles (PBAP). Atmosphere 9(1): 1. doi:10.3390/atmos9010001.
   Web of Science ®Google Scholar
• Fernández-Llamazares Á, Belmonte J, Delgado R, De Linares C. 2014. A statistical approach to bioclimatic trend detection in the airborne pollen records of Catalonia (NE Spain). International Journal of Biometeorology 58(3): 371‒382. doi:10.1007/s00484-013-0632-4.
   PubMed Web of Science ®Google Scholar
• Fernández-Rodríguez S, Durán-Barroso P, Silva-Palacios I, Tormo-Molina R, Maya-Manzano JM, Gonzalo-Garijo Á. 2016a. Forecast model of allergenic hazard using trends of Poaceae airborne pollen over an urban area in SW Iberian Peninsula (Europe). Natural Hazards 84(1): 121‒137. doi:10.1007/s11069-016-2411-0.
   Web of Science ®Google Scholar
• Fernández-Rodríguez S, Durán-Barroso P, Silva-Palacios I, Tormo-Molina R, Maya-Manzano JM, Gonzalo-Garijo Á. 2016b. Quercus long-term pollen season trends in the southwest of the Iberian Peninsula. Process Safety and Environmental Protection 101: 152‒159. doi:10.1016/j.psep.2015.11.008.
   Web of Science ®Google Scholar
• Fernández-Rodríguez S, Durán-Barroso P, Silva-Palacios I, Tormo-Molina R, Maya-Manzano JM, Gonzalo-Garijo Á. 2016c. Regional forecast model for the Olea pollen season in Extremadura (SW Spain). International Journal of Biometeorology 60(10): 1509‒1517. doi:10.1007/s00484-016-1141-z.
   Web of Science ®Google Scholar
• Fernández-Rodríguez S, Durán-Barroso P, Silva-Palacios I, Tormo-Molina R, Maya-Manzano JM, Gonzalo-Garijo A, Monroy-Colín A. 2018. Environmental assessment of allergenic risk provoked by airborne grass pollen through forecast model in a mediterranean region. Journal of Cleaner Production 176: 1304‒1315. doi:10.1016/j.jclepro.2017.11.226.
   PubMed Web of Science ®Google Scholar
• Fernández-Rodríguez S, Skjøth CA, Tormo-Molina R, Brandao R, Caeiro E, Silva-Palacios I, Gonzalo-Garijo Á, Smith M. 2014. Identification of potential sources of airborne Olea pollen in the Southwest Iberian Peninsula. International Journal of Biometeorology 58(3): 337‒348. doi:10.1007/s00484-012-0629-4.
   PubMed Web of Science ®Google Scholar
• Frenguelli G, Ghitarrini S, Tedeschini E. 2016. Time linkages between pollination onsets of different taxa in Perugia, central Italy – an update. Annals of Agricultural and Environmental Medicine 23: 92‒96.
   PubMed Web of Science ®Google Scholar
• Galan C, Antunes C, Brandao R, Torres C, Garcia-Mozo H, Caeiro E, Ferro R, Prank M, Sofiev M, Albertini R, Berger U, Cecchi L, Celenk S, Grewling L, Jackowiak B, Jäger S, Kennedy R, Rantio-Lehtimäki A, Reese G, Sauliene I, Smith M, Thibaudon M, Weber B, Weichenmeier I, Pusch G, Buters JTM. 2013. Airborne olive pollen counts are not representative of exposure to the major olive allergen Ole e 1. Allergy: European Journal of Allergy and Clinical Immunology 68(6): 809‒812. doi:10.1111/all.12144.
   Web of Science ®Google Scholar
• Galán C, Ariatti A, Bonini M, Clot B, Crouzy B, Dahl A, Fernandez-González D, Frenguelli G, Gehrig R, Isard S, Levetin E, Li DW, Mandrioli P, Rogers CA, Thibaudon M, Sauliene I, Skjoth C, Smith M, Sofiev M. 2017. Recommended terminology for aerobiological studies. Aerobiologia 33(3): 293‒295. doi:10.1007/s10453-017-9496-0.
   Web of Science ®Google Scholar
• Galán C, Smith M, Thibaudon M, Frenguelli G, Oteros J, Gehrig R, Berger U, Clot B, Brandao R. 2014. Pollen monitoring: Minimum requirements and reproducibility of analysis. Aerobiologia 30(4): 385‒395. doi:10.1007/s10453-014-9335-5.
   Web of Science ®Google Scholar
• Galera MD, Elvira - Rendueles B, Moreno JM, Negral L, Ruiz-Abellón MC, García-Sánchez A, Moreno-Grau S. 2018. Analysis of airborne Olea pollen in Cartagena (Spain). Science of the Total Environment 622‒623: 436‒445.
   PubMed Web of Science ®Google Scholar
• García-Mozo H, Oteros J, Galán C. 2015. Phenological changes in olive (Ola europaea L.) reproductive cycle in southern Spain due to climate change. Annals of Agricultural and Environmental Medicine 22(3): 421‒428. doi:10.5604/12321966.1167706.
   PubMed Web of Science ®Google Scholar
• García-Mozo H, Yaezel L, Oteros J, Galán C. 2014. Statistical approach to the analysis of olive long-term pollen season trends in southern Spain. Science of the Total Environment 473‒474: 103‒109.
   PubMed Web of Science ®Google Scholar
• González-Naharro R, Quirós E, Fernández-Rodríguez S, Silva-Palacios I, Maya-Manzano JM, Tormo-Molina R, Pecero-Casimiro R, Monroy-Colin A, Gonzalo-Garijo Á. 2019. Relationship of NDVI and oak (Quercus) pollen including a predictive model in the SW Mediterranean region. Science of the Total Environment 676: 407‒419. doi:10.1016/j.scitotenv.2019.04.213.
   PubMed Web of Science ®Google Scholar
• Grewling Ł, Bogawski P, Jenerowicz D, Czarnecka-Operacz M, Šikoparija B, Skjøth CA, Smith M. 2016. Mesoscale atmospheric transport of ragweed pollen allergens from infected to uninfected areas. International Journal of Biometeorology 60(10): 1493‒1500. doi:10.1007/s00484-016-1139-6.
   Web of Science ®Google Scholar
• Grinnell SW, Perkins WA, Vaughan LM. 1961. Sampling apparatus and method. Patent no. 2,973,642. Washington: United States Patent Office.
   Google Scholar
• Healy D, Huffman J, O’Connor DJ, Pöhlker C, Pöschl U, Sodeau J. 2014. Ambient measurements of biological aerosol particles near Killarney, Ireland: A comparison between real-time fluorescence and microscopy techniques. Atmospheric Chemistry and Physics 14(15): 8055‒8069. doi:10.5194/acp-14-8055-2014.
   Web of Science ®Google Scholar
• Hernandez-Ceballos MA, Soares J, García-Mozo H, Sofiev M, Bolivar JP, Galán C. 2014. Analysis of atmospheric dispersion of olive pollen in southern Spain using SILAM and HYSPLIT models. Aerobiologia 30(3): 239‒255. doi:10.1007/s10453-013-9324-0.
   Web of Science ®Google Scholar
• Hirst JM. 1952. An automatic volumetric spore trap. The Annals of Applied Biology 39(2): 257‒265. doi:10.1111/j.1744-7348.1952.tb00904.x.
   Web of Science ®Google Scholar
• Hjelmroos M. 1991. Evidence of long distance transport of Betula pollen. Grana 30: 215‒228.
   Web of Science ®Google Scholar
• Hjelmroos M. 1992. Long-distance transport ofBetula pollen grains and allergic symptoms. Aerobiologia 8(2): 231‒236. doi:10.1007/BF02071631.
   Google Scholar
• Howard LE, Levetin E. 2014. Ambrosia pollen in Tulsa, Oklahoma: Aerobiology, trends, and forecasting model development. Annals of Allergy, Asthma and Immunology 113(6): 641‒646. doi:10.1016/j.anai.2014.08.019.
   PubMed Web of Science ®Google Scholar
• Huffman JA, Perring AE, Savage NJ, Clot B, Crouzy B, Tummon F, Shoshanim O, Damit B, Schneider J, Sivaprakasam V, Zawadowicz MA, Crawford I, Gallagher M, Topping D, Doughty DC, Hill SC, Pan Y. 2020. Real-time sensing of bioaerosols: Review and current perspectives. Aerosol Science and Technology 54(5): 465‒495. doi:10.1080/02786826.2019.1664724.
   PubMed Web of Science ®Google Scholar
• Iglesias-Otero MA, Astray G, Vara A, Galvez JF, Mejuto JC, Rodriguez-Rajo FJ. 2015. Forecasting Olea airborne pollen concentration by means of artificial intelligence. Fresenius Environmental Bulletin 24: 4574‒4580.
   Web of Science ®Google Scholar
• Inatsu M, Kobayashi S, Tekeuchi S, Ohmori A. 2014. Statistical analysis on daily variations of Birch pollen amount with climatic variables in Sapporo. SOLA 10: 172‒175. doi:10.2151/sola.2014-036.
   Web of Science ®Google Scholar
• Izquierdo R, Alarcón M, Mazón J, Pino D, De Linares C, Aguinagalde X, Belmonte J. 2017. Are the Pyrenees a barrier for the transport of birch (Betula) pollen from Central Europe to the Iberian Peninsula? Science of the Total Environment 575: 1183‒1196. doi:10.1016/j.scitotenv.2016.09.192.
   PubMed Web of Science ®Google Scholar
• Janati A, Bouziane H, del Mar Trigo M, Kadiri M, Kazzaz M. 2017. Poaceae pollen in the atmosphere of Tetouan (NW Morocco): Effect of meteorological parameters and forecast of daily pollen concentration. Aerobiologia 33(4): 517‒528. doi:10.1007/s10453-017-9487-1.
   Web of Science ®Google Scholar
• Jato V, Rodríguez-Rajo FJ, Alcázar P, De Nuntiis P, Galán C, Mandrioli P. 2006. May the definition of pollen season influence aerobiological results? Aerobiologia 22(1): 13‒25. doi:10.1007/s10453-005-9011-x.
   Web of Science ®Google Scholar
• Jato V, Rodríguez-Rajo FJ, Fernández-González M, Aira MJ. 2014. Assessment of Quercus flowering trends in NW Spain. International Journal of Biometeorology 59: 517‒531.
   PubMed Web of Science ®Google Scholar
• Jeon W, Choi Y, Roy A, Pan S, Price D, Hwang M-K, Kim KR, Oh I. 2018. Investigation of primary factors affecting the variation of modeled Oak Pollen concentrations: A case study for Southeast Texas in 2010. Asia-Pacific Journal of Atmospheric Sciences 54(1): 33‒41. doi:10.1007/s13143-017-0057-9.
   PubMed Web of Science ®Google Scholar
• Jochner-Oette S, Menzel A, Gehrig R, Clot B. 2019. Decrease or increase? Temporal changes in pollen concentrations assessed by Bayesian statistics. Aerobiologia 35(1): 153‒163. doi:10.1007/s10453-018-9547-1.
   Web of Science ®Google Scholar
• Karatzas KD, Riga M, Smith M. 2013. Presentation and dissemination of pollen information. In: Sofiev M, Bergmann K-C, eds. Allergenic pollen, 217‒247. Dordrecht: Springer Science+Business Media.
   Google Scholar
• Karrer G, Skjøth CA, Šikoparija B, Smith M, Berger U, Essl F. 2015. Ragweed (Ambrosia) pollen source inventory for Austria. Science of the Total Environment 523: 120‒128. doi:10.1016/j.scitotenv.2015.03.108.
   PubMed Web of Science ®Google Scholar
• Kasprzyk I, Myszkowska D, Grewling Ł, Stach A, Šikoparija B, Skjøth CA, Smith M. 2011. The occurrence of Ambrosia pollen in Rzeszów, Kraków and Poznań, Poland: Investigation of trends and possible transport of Ambrosia pollen from Ukraine. International Journal of Biometeorology 55(4): 633‒644. doi:10.1007/s00484-010-0376-3.
   PubMed Web of Science ®Google Scholar
• Katz DSW, Batterman SA. 2019. Allergenic pollen production across a large city for common ragweed (Ambrosia artemisiifolia). Landscape and Urban Planning 190: 103615. doi:10.1016/j.landurbplan.2019.103615.
   PubMed Web of Science ®Google Scholar
• Kawashima S, Clot B, Fujita T, Takahashi Y, Nakamura K. 2007. An algorithm and a device for counting airborne pollen automatically using laser optics. Atmospheric Environment 41(36): 7987‒7993. doi:10.1016/j.atmosenv.2007.09.019.
   Web of Science ®Google Scholar
• Kinnear PR, Gray CD. 1999. SPSS for windows made simple. Abingdon: Taylor & Francis.
   Google Scholar
• Lara B, Rojo J, Fernández-González F, Pérez-Badia R. 2019. Prediction of airborne pollen concentrations for the plane tree as a tool for evaluating allergy risk in urban green areas. Landscape and Urban Planning 189: 285‒295. doi:10.1016/j.landurbplan.2019.05.002.
   Web of Science ®Google Scholar
• Linkosalo T, Lappalainen HK, Hari P. 2008. A comparison of phenological models of leaf bud burst and flowering of boreal trees using independent observations. Tree Physiology 28(12): 1873–1882. doi:10.1093/treephys/28.12.1873.
   PubMed Web of Science ®Google Scholar
• Linkosalo T, Le Tortorec E, Prank M, Pessi A-M, Saarto A. 2017. Alder pollen in Finland ripens after a short exposure to warm days in early spring, showing biennial variation in the onset of pollen ripening. Agricultural and Forest Meteorology 247: 408‒413. doi:10.1016/j.agrformet.2017.08.030.
   Web of Science ®Google Scholar
• Linkosalo T, Ranta H, Oksanen A, Siljamo P, Luomajoki A, Kukkonen J, Sofiev M. 2010. A double-threshold temperature sum model for predicting the flowering duration and relative intensity of Betula pendula and B. pubescens. Agricultural and Forest Meteorology 150(12): 1579‒1584. doi:10.1016/j.agrformet.2010.08.007.
   Web of Science ®Google Scholar
• Liu L, Solmon F, Vautard R, Hamaoui-Laguel L, Torma CZ, Giorgi F. 2016. Ragweed pollen production and dispersion modelling within a regional climate system, calibration and application over Europe. Biogeosciences 13(9): 2769‒2786. doi:10.5194/bg-13-2769-2016.
   PubMed Web of Science ®Google Scholar
• Liu X, Wu D, Zewdie GK, Wijerante L, Timms CI, Riley A, Levetin E, Lary DJ. 2017. Using machine learning to estimate atmospheric Ambrosia pollen concentrations in Tulsa, OK. Environmental Health Insights 11: 1178630217699399. doi:10.1177/1178630217699399.
   Web of Science ®Google Scholar
• Lukasiewicz A. 1984. Need to standardize phenological methodology in Polish botanic gardens and arboretums. Botanic News 28: 153–158.
   Google Scholar
• Mahura A, Baklanov A, Korsholm U. 2009. Parameterization of the birch pollen diurnal cycle. Aerobiologia 25(4): 203–208. doi:10.1007/s10453-009-9125-7.
   Web of Science ®Google Scholar
• Malkiewicz M, Klaczak K, Drzeniecka-Osiadacz A, Krynicka J, Migała K. 2014. Types of Artemisia pollen season depending on the weather conditions in Wrocław (Poland), 2002–2011. Aerobiologia 30(1): 13–23. doi:10.1007/s10453-013-9304-4.
   PubMed Web of Science ®Google Scholar
• Maya-Manzano JM, Fernández-Rodríguez S, Smith M, Tormo-Molina R, Reynolds A, Silva-Palacios I, Gonzalo-Garijo Á, Sadys' M. 2016. Airborne Quercus pollen in SW Spain: Identifying favourable conditions for atmospheric transport and potential source areas. Science of the Total Environment 571: 1037–1047. doi:10.1016/j.scitotenv.2016.07.094.
   PubMed Web of Science ®Google Scholar
• Murray MG, Galan C. 2016. Effect of the meteorological parameters on the Olea europaea L. pollen season in Bahía Blanca (Argentina). Aerobiologia 32(3): 541–553. doi:10.1007/s10453-016-9431-9.
   Web of Science ®Google Scholar
• Myszkowska D. 2013. Prediction of the birch pollen season characteristics in Cracow, Poland using an 18-year data series. Aerobiologia 29(1): 31–44. doi:10.1007/s10453-012-9260-4.
   PubMed Web of Science ®Google Scholar
• Myszkowska D. 2014a. Poaceae pollen in the air depending on the thermal conditions. International Journal of Biometeorology 58(5): 975–986. doi:10.1007/s00484-013-0682-7.
   PubMed Web of Science ®Google Scholar
• Myszkowska D. 2014b. Predicting tree pollen season start dates using thermal conditions. Aerobiologia 30(3): 307–321. doi:10.1007/s10453-014-9329-3.
   PubMed Web of Science ®Google Scholar
• Myszkowska D, Majewska R. 2014. Pollen grains as allergenic environmental factors – New approach to the forecasting of the pollen concentration during the season. Annals of Agricultural and Environmental Medicine 21(4): 681–688. doi:10.5604/12321966.1129914.
   PubMed Web of Science ®Google Scholar
• Navares R, Aznarte JL. 2017. Predicting the Poaceae pollen season: Six month-ahead forecasting and identification of relevant features. International Journal of Biometeorology 61(4): 647–656. doi:10.1007/s00484-016-1242-8.
   PubMed Web of Science ®Google Scholar
• Navares R, Aznarte JL. 2020. Forecasting Plantago pollen: Improving feature selection through random forests, clustering, and Friedman tests. Theoretical and Applied Climatology 139(1–2): 163–174. doi:10.1007/s00704-019-02954-1.
   Web of Science ®Google Scholar
• Noll KE. 1970. A rotary inertial impactor for sampling giant particles in the atmosphere. Atmospheric Environment (1967) 4(1): 9–19. doi:10.1016/0004-6981(70)90050-8.
   Web of Science ®Google Scholar
• Novara C, Falzoi S, La Morgia V, Spanna F, Siniscalco C. 2016. Modelling the pollen season start in Corylus avellana and Alnus glutinosa. Aerobiologia 32(3): 555–569. doi:10.1007/s10453-016-9432-8.
   Web of Science ®Google Scholar
• Nowosad J. 2016. Spatiotemporal models for predicting high pollen concentration level of Corylus, Alnus, and Betula. International Journal of Biometeorology 60(6): 843–855. doi:10.1007/s00484-015-1077-8.
   PubMed Web of Science ®Google Scholar
• Nowosad J, Stach A, Kasprzyk I, Chłopek K, Dąbrowska-Zapart K, Grewling Ł, Latałowa M, Pędziszewska A, Majkowska-Wojciechowska B, Myszkowska D. 2018. Statistical techniques for modeling of Corylus, Alnus, and Betula pollen concentration in the air. Aerobiologia 34(3): 301–313. doi:10.1007/s10453-018-9514-x.
   Web of Science ®Google Scholar
• Nowosad J, Stach A, Kasprzyk I, Weryszko-Chmielewska E, Piotrowska-Weryszko K, Puc M, Grewling Ł, Pędziszewska A, Uruska A, Myszkowska D. 2016. Forecasting model of Corylus, Alnus, and Betula pollen concentration levels using spatiotemporal correlation properties of pollen count. Aerobiologia 32(3): 453–468. doi:10.1007/s10453-015-9418-y.
   PubMed Web of Science ®Google Scholar
• O’Connor DJ, Daly SM, Sodeau JR. 2015. On-line monitoring of airborne bioaerosols released from a composting/green waste site. Waste Management 42: 23–30. doi:10.1016/j.wasman.2015.04.015.
   PubMed Web of Science ®Google Scholar
• O’Connor DJ, Healy DA, Hellebust S, Buters JT, Sodeau JR. 2014. Using the WIBS-4 (Waveband Integrated Bioaerosol Sensor) technique for the on-line detection of pollen grains. Aerosol Science and Technology 48(4): 341–349. doi:10.1080/02786826.2013.872768.
   Web of Science ®Google Scholar
• Ocaña-Peinado FM, Valderrama MJ, Bouzas PR. 2013. A principal component regression model to forecast airborne concentration of Cupressaceae pollen in the city of Granada (SE Spain), during 1995-2006. International Journal of Biometeorology 57(3): 483–486. doi:10.1007/s00484-012-0527-9.
   PubMed Web of Science ®Google Scholar
• Oteros J, Bergmann K-C, Menzel A, Damialis A, Traidl-Hoffmann C, Schmidt-Weber CB, Buters J. 2019a. Spatial interpolation of current airborne pollen concentrations where no monitoring exists. Atmospheric Environment 199: 435–442. doi:10.1016/j.atmosenv.2018.11.045.
   Web of Science ®Google Scholar
• Oteros J, Buters J, Laven G, Röseler S, Wachter R, Schmidt-Weber C, Hofmann F. 2017a. Errors in determining the flow rate of Hirst-type pollen traps. Aerobiologia 33(2): 201–210. doi:10.1007/s10453-016-9467-x.
   Web of Science ®Google Scholar
• Oteros J, García-Mozo H, Hervás C, Galán C. 2013a. Biometeorological and autoregressive indices for predicting olive pollen intensity. International Journal of Biometeorology 57(2): 307–316. doi:10.1007/s00484-012-0555-5.
   PubMed Web of Science ®Google Scholar
• Oteros J, García-Mozo H, Hervás-Martínez C, Galán C. 2013b. Year clustering analysis for modelling olive flowering phenology. International Journal of Biometeorology 57(4): 545–555. doi:10.1007/s00484-012-0581-3.
   PubMed Web of Science ®Google Scholar
• Oteros J, Orlandi F, García-Mozo H, Aguilera F, Dhiab AB, Bonofiglio T, Abichou M, Ruiz-Valenzuela L, Del Trigo MM, Díaz De La Guardia C, Domínguez-Vilches E, Msallem M, Fornaciari M, Galán C. 2014. Better prediction of mediterranean olive production using pollen-based models. Agronomy for Sustainable Development 34: 685–694.
   Web of Science ®Google Scholar
• Oteros J, Pusch G, Weichenmeier I, Heimann U, Möller R, Röseler S, Traidl-Hoffmann C, Schmidt-Weber C, Buters JTM. 2015. Automatic and online pollen monitoring. International Archives of Allergy and Immunology 167(3): 158–166. doi:10.1159/000436968.
   PubMed Web of Science ®Google Scholar
• Oteros J, Sofiev M, Smith M, Clot B, Damialis A, Prank M, Werchan M, Wachter R, Weber A, Kutzora S, Heinze S, Herr CEW, Menzel A, Bergmann K-C, Traidl-Hoffmann C, Schmidt-Weber CB, Buters JTM. 2019b. Building an automatic pollen monitoring network (ePIN): Selection of optimal sites by clustering pollen stations. Science of the Total Environment 688: 1263–1274. doi:10.1016/j.scitotenv.2019.06.131.
   PubMed Web of Science ®Google Scholar
• Oteros J, Valencia RM, del Río S, Vega AM, García-Mozo H, Galán C, Gutiérrez P, Mandrioli P, Fernández-González D. 2017b. Concentric ring method for generating pollen maps. Quercus as Case Study. Science of the Total Environment 576: 637–645. doi:10.1016/j.scitotenv.2016.10.121.
   PubMed Web of Science ®Google Scholar
• Ottosen T-B, Petch G, Hanson M, Skjøth CA. 2020. Tree cover mapping based on Sentinel-2 images demonstrate high thematic accuracy in Europe. International Journal of Applied Earth Observation and Geoinformation 84: 101947. doi:10.1016/j.jag.2019.101947.
   PubMed Web of Science ®Google Scholar
• Pauling A, Clot B, Menzel A, Jung S. 2020. Pollen forecasts in complex topography: Two case studies from the Alps using the numerical pollen forecast model COSMO-ART. Aerobiologia 36(1): 25–30. doi:10.1007/s10453-019-09590-2.
   Web of Science ®Google Scholar
• Pauling A, Gehrig R, Clot B. 2014. Toward optimized temperature sum parameterizations for forecasting the start of the pollen season. Aerobiologia 30(1): 45–57. doi:10.1007/s10453-013-9308-0.
   Web of Science ®Google Scholar
• Pauling A, Rotach MW, Gehrig R, Clot B, EAN. 2012. A method to derive vegetation distribution maps for pollen dispersion models using birch as an example. International Journal of Biometeorology 56(5): 949–958. doi:10.1007/s00484-011-0505-7.
   PubMed Web of Science ®Google Scholar
• Pecero-Casimiro R, Fernández-Rodríguez S, Tormo-Molina R, Monroy-Colín A, Silva-Palacios I, Cortés-Pérez JP, Gonzalo-Garijo Á, Maya-Manzano JM. 2019. Urban aerobiological risk mapping of ornamental trees using a new index based on LiDAR and Kriging: A case study of plane trees. Science of the Total Environment 693: 133576. doi:10.1016/j.scitotenv.2019.07.382.
   PubMed Web of Science ®Google Scholar
• Picornell A, Buters J, Rojo J, Traidl-Hoffmann C, Menzel A, Bergmann K, Werchan M, Schmidt-Weber C, Oteros J. 2019a. Predicting the start, peak and end of the Betula pollen season in Bavaria, Germany. Science of the Total Environment 690: 1299–1309. doi:10.1016/j.scitotenv.2019.06.485.
   PubMed Web of Science ®Google Scholar
• Picornell A, Oteros J, Trigo M, Gharbi D, Fernández SD, Caballero MM, Toro F, García-Sánchez J, Ruiz-Mata R, Cabezudo B. 2019b. Increasing resolution of airborne pollen forecasting at a discrete sampled area in the southwest mediterranean Basin. Chemosphere 234: 668–681. doi:10.1016/j.chemosphere.2019.06.019.
   PubMed Web of Science ®Google Scholar
• Piotrowska K. 2012. Forecasting the Poaceae pollen season in eastern Poland. Grana 51(4): 263–269. doi:10.1080/00173134.2012.659204.
   Web of Science ®Google Scholar
• Piotrowska-Weryszko K. 2013a. Artemisia pollen in the air of Lublin, Poland (2001–2012). Acta Scientiarum Polonorum-Hortorum Cultus 12: 155–168.
   Web of Science ®Google Scholar
• Piotrowska-Weryszko K. 2013b. The effect of the meteorological factors on the Alnus pollen season in Lublin (Poland). Grana 52(3): 221–228. doi:10.1080/00173134.2013.772653.
   Web of Science ®Google Scholar
• Pöhlker C, Huffman J, Pöschl U. 2012. Autofluorescence of atmospheric bioaerosols–fluorescent biomolecules and potential interferences. Atmospheric Measurement Techniques 5(1): 37–71. doi:10.5194/amt-5-37-2012.
   Web of Science ®Google Scholar
• Prank M, Chapman DS, Bullock JM, Belmonte J, Berger U, Dahl A, Jäger S, Kovtunenko I, Magyar D, Niemelä S, Rantio-Lehtimäki A, Rodinkova V, Sauliene I, Severova E, Sikoparija B, Sofiev M. 2013. An operational model for forecasting ragweed pollen release and dispersion in Europe. Agricultural and Forest Meteorology 182-183: 43–53. doi:10.1016/j.agrformet.2013.08.003.
   Web of Science ®Google Scholar
• Prank M, Sofiev M, Siljamo P, Kauhaniemi M. 2016. Increasing the number of allergenic pollen species in SILAM forecasts. In: Steyn D, Chaumerliac N, eds. Air pollution modeling and its application XXIV, 313–317. Springer Proceedings in Complexity.
   Google Scholar
• Puc M. 2012. Artificial neural network model of the relationship between Betula pollen and meteorological factors in Szczecin (Poland). International Journal of Biometeorology 56(2): 395–401. doi:10.1007/s00484-011-0446-1.
   PubMed Web of Science ®Google Scholar
• Puc M, Wolski T. 2013. Forecasting of the selected features of Poaceae (R. Br.) Barnh., Artemisia L. and Ambrosia L. pollen season in Szczecin, north-western Poland, using Gumbel’s distribution. Annals of Agricultural and Environmental Medicine 20(1): 36–47.
   PubMed Web of Science ®Google Scholar
• Qin X, Li Y, Sun X, Meng L, Wang X. 2019. Transport pathway and source area for Artemisia pollen in Beijing, China. International Journal of Biometeorology 63(5): 687–699. doi:10.1007/s00484-017-1467-1.
   PubMed Web of Science ®Google Scholar
• Recknagel F. 2001. Applications of machine learning to ecological modelling. Ecological Modelling 146(1–3): 303–310. doi:10.1016/S0304-3800(01)00316-7.
   Web of Science ®Google Scholar
• Ritenberga O, Sofiev M, Kirillova V, Kalnina L, Genikhovich E. 2016. Statistical modelling of non-stationary processes of atmospheric pollution from natural sources: Example of birch pollen. Agricultural and Forest Meteorology 226: 96–107. doi:10.1016/j.agrformet.2016.05.016.
   Web of Science ®Google Scholar
• Robichaud A, Comtois P. 2017. Statistical modeling, forecasting and time series analysis of birch phenology in Montreal, Canada. Aerobiologia 33(4): 529–554. doi:10.1007/s10453-017-9488-0.
   Web of Science ®Google Scholar
• Rodríguez-Rajo FJ, Astray G, Ferreiro-Lage JA, Aira MJ, Jato-Rodriguez MV, Mejuto JC. 2010. Evaluation of atmospheric Poaceae pollen concentration using a neural network applied to a coastal Atlantic climate region. Neural Networks 23(3): 419–425. doi:10.1016/j.neunet.2009.06.006.
   PubMed Web of Science ®Google Scholar
• Rojo J, Orlandi F, Pérez-Badia R, Aguilera F, Ben Dhiab A, Bouziane H, Díaz de la Guardia C, Galán C, Gutiérrez-Bustillo AM, Moreno-Grau S, Msallem M, Trigo MM, Fornaciari M. 2016. Modeling olive pollen intensity in the mediterranean region through analysis of emission sources. Science of the Total Environment 551–552: 73–82. doi:10.1016/j.scitotenv.2016.01.193.
   PubMed Web of Science ®Google Scholar
• Rojo J, Rivero R, Romero-Morte J, Fernández-González F, Pérez-Badia R. 2017. Modeling pollen time series using seasonal-trend decomposition procedure based on LOESS smoothing. International Journal of Biometeorology 61(2): 335–348. doi:10.1007/s00484-016-1215-y.
   PubMed Web of Science ®Google Scholar
• Roshchina V. 2008. Fluorescing world of plant secreting cells. Boca Raton, FL: CRC Press.
   Google Scholar
• Sabariego S, Cuesta P, Fernández-González F, Pérez-Badia R. 2012. Models for forecasting airborne Cupressaceae pollen levels in central Spain. International Journal of Biometeorology 56(2): 253–258. doi:10.1007/s00484-011-0423-8.
   PubMed Web of Science ®Google Scholar
• Sánchez Mesa JA, Galán C, Hervás C. 2005. The use of discriminant analysis and neural networks to forecast the severity of the Poaceae pollen season in a region with a typical mediterranean climate. International Journal of Biometeorology 49(6): 355–362. doi:10.1007/s00484-005-0260-8.
   PubMed Web of Science ®Google Scholar
• Sánchez-Mesa JA, Galan C, Martínez-Heras JA, Hervás-Martínez C. 2002. The use of a neural network to forecast daily grass pollen concentration in a mediterranean region: The southern part of the Iberian Peninsula. Clinical & Experimental Allergy 32(11): 1606–1612. doi:10.1046/j.1365-2222.2002.01510.x.
   PubMedGoogle Scholar
• Sauvageat E, Zeder Y, Auderset K, Calpini B, Clot B, Crouzy B, Konzelmann T, Lieberherr G, Tummon F, Vasilatou K. 2020. Real-time pollen monitoring using digital holography. Atmospheric Measuring Techiques Discussions 13: 1539–1550.
   Web of Science ®Google Scholar
• Scheifinger H, Belmonte J, Buters J, Celenk S, Damialis A, Dechamp C, García-Mozo H, Gehrig R, Grewling L, Halley JM, Hogda K-A, Jäger S, Karatzas K, Karlsen S-R, Koch E, Pauling A, Peel R, Sikoparija B, Smith M, Galán-Soldevilla C, Thibaudon M, Vokou D, Weger LA. 2013. Monitoring, modelling and forecasting of the Pollen season. In: Sofiev M, Bergmann KC, eds.. Allergenic pollen, 71–126. Dordrecht: Springer Science+Business Media.
   Google Scholar
• Sicard P, Thibaudon M, Besancenot J-P, Mangin A. 2012. Forecast models and trends for the main characteristics of the Olea pollen season in Nice (south-eastern France) over the 1990–2009 period. Grana 51(1): 52–62. doi:10.1080/00173134.2011.637577.
   Web of Science ®Google Scholar
• Sikoparija B, Galán C, Smith M, EAS_QC_Working_Group. 2017a. Pollen-monitoring: Between analyst proficiency testing. Aerobiologia 33(2): 191–199. doi:10.1007/s10453-016-9461-3.
   Web of Science ®Google Scholar
• Šikoparija B, Pejak-Šikoparija T, Radišić P, Smith M, Galan-Soldevilla C. 2011. The effect of changes to the method of estimating the pollen count from aerobiological samples. Journal of Environmental Monitoring 13(2): 384–390. doi:10.1039/C0EM00335B.
   PubMedGoogle Scholar
• Šikoparija B, Marko O, Panić M, Jakovetić D, Radišić P. 2018a. How to prepare a pollen calendar for forecasting daily pollen concentrations of Ambrosia. Betula and Poaceae? Aerobiologia 34(2): 203–217. doi:10.1007/s10453-018-9507-9.
   Web of Science ®Google Scholar
• Šikoparija B, Mimić G, Panić M, Marko O, Radišić P, Pejak-Šikoparija T, Pauling A. 2018b. High temporal resolution of airborne Ambrosia pollen measurements above the source reveals emission characteristics. Atmospheric Environment 192: 13–23. doi:10.1016/j.atmosenv.2018.08.040.
   Web of Science ®Google Scholar
• Sikoparija B, Skjøth CA, Celenk S, Testoni C, Abramidze T, Alm Kübler K, Belmonte J, Berger U, Bonini M, Charalampopoulos A, Damialis A, Clot B, Dahl Å, de Weger LA, Gehrig R, Hendrickx M, Hoebeke L, Ianovici N, Kofol Seliger A, Magyar D, Mányoki G, Milkovska S, Myszkowska D, Páldy A, Pashley CH, Rasmussen K, Ritenberga O, Rodinkova V, Rybníček O, Shalaboda V, Šaulienė I, Ščevková J, Stjepanović B, Thibaudon M, Verstraeten C, Vokou D, Yankova R, Smith M. 2017b. Spatial and temporal variations in airborne Ambrosia pollen in Europe. Aerobiologia 33(2): 181–189. doi:10.1007/s10453-016-9463-1.
   PubMed Web of Science ®Google Scholar
• Sikoparija B, Smith M, Skjoth CA, Radisic P, Milkovska S, Simic S, Brandt J. 2009. The Pannonian Plain as a source of Ambrosia pollen in the Balkans. International Journal of Biometeorology 53(3): 263–272. doi:10.1007/s00484-009-0212-9.
   PubMed Web of Science ®Google Scholar
• Siljamo P, Sofiev M, Filatova E, Grewling Ł, Jäger S, Khoreva E, Linkosalo T, Ortega Jimenez S, Ranta H, Rantio-Lehtimäki A, Svetlov A, Veriankaite L, Yakovleva E, Kukkonen J. 2013. A numerical model of birch pollen emission and dispersion in the atmosphere. Model evaluation and sensitivity analysis. International Journal of Biometeorology 57(1): 125–136. doi:10.1007/s00484-012-0539-5.
   PubMed Web of Science ®Google Scholar
• Silva-Palacios I, Fernández-Rodríguez S, Durán-Barroso P, Tormo-Molina R, Maya-Manzano JM, Gonzalo-Garijo Á. 2016. Temporal modelling and forecasting of the airborne pollen of Cupressaceae on the southwestern Iberian Peninsula. International Journal of Biometeorology 60(2): 297–306. doi:10.1007/s00484-015-1026-6.
   PubMed Web of Science ®Google Scholar
• Singh KP, Gupta S, Rai P. 2013. Identifying pollution sources and predicting urban air quality using ensemble learning methods. Atmospheric Environment 80: 426–437. doi:10.1016/j.atmosenv.2013.08.023.
   Web of Science ®Google Scholar
• Siniscalco C, Caramiello R, Migliavacca M, Busetto L, Mercalli L, Colombo R, Richardson AD. 2015. Models to predict the start of the airborne pollen season. International Journal of Biometeorology 59(7): 837–848. doi:10.1007/s00484-014-0901-x.
   PubMed Web of Science ®Google Scholar
• Skjøth CA, Baker P, Sadyś M, Adams-Groom B. 2015a. Pollen from alder (Alnus sp.), birch (Betula sp.) and oak (Quercus sp.) in the UK originate from small woodlands. Urban Climate 14: 414–428. doi:10.1016/j.uclim.2014.09.007.
   Web of Science ®Google Scholar
• Skjøth CA, Bilińska D, Werner M, Malkiewicz M, Adams-Groom B, Kryza M, Drzeniecka-Osiadacz A. 2015b. Footprint areas of pollen from alder (Alnus) and birch (Betula) in the UK (Worcester) and Poland (Wroclaw) during 2005-2014. Acta Agrobotanica 68(4): 315–324. doi:10.5586/aa.2015.044.
   Google Scholar
• Skjøth CA, Geels C, Hvidberg M, Hertel O, Brandt J, Frohn LM, Hansen KM, Hedegård GB, Christensen J, Moseholm L. 2008a. An inventory of tree species in Europe – an essential data input for air pollution modelling. Ecological Modelling 217(3–4): 292–304. doi:10.1016/j.ecolmodel.2008.06.023.
   Web of Science ®Google Scholar
• Skjøth CA, Šikoparija B, Jäger S, EAN. 2013. Pollen sources. In: Sofiev M, Bergmann KC, eds. Allergenic pollen, 9–27. Dordrecht: Springer Science+Business Media.
   Google Scholar
• Skjøth CA, Smith M, Brandt J, Emberlin J. 2009. Are the birch trees in Southern England a source of Betula pollen for North London? International Journal of Biometeorology 53(1): 75–86. doi:10.1007/s00484-008-0192-1.
   PubMed Web of Science ®Google Scholar
• Skjøth CA, Smith M, Šikoparija B, Stach A, Myszkowska D, Kasprzyk I, Radišić P, Stjepanović B, Hrga I, Apatini D, Magyar D, Páldy A, Ianovici N. 2010. A method for producing airborne pollen source inventories: An example of Ambrosia (ragweed) on the Pannonian plain. Agricultural and Forest Meteorology 150(9): 1203–1210. doi:10.1016/j.agrformet.2010.05.002.
   Web of Science ®Google Scholar
• Skjøth CA, Sommer J, Brandt J, Hvidberg M, Geels C, Hansen KM, Hertel O, Frohn LM, Christensen JH. 2008b. Copenhagen - significant source to birch (Betula) pollen? International Journal of Biometeorology 52(6): 453–462. doi:10.1007/s00484-007-0139-y.
   PubMed Web of Science ®Google Scholar
• Skjøth CA, Werner M, Kryza M, Adams-Groom B, Wakeham A, Lewis M, Kennedy R. 2015c. Quality of the governing temperature variables in WRF in relation to simulation of primary biological aerosols. Advances in Meteorology 2015: 1–15. doi:10.1155/2015/412658.
   Web of Science ®Google Scholar
• Smith M, Jäger S, Berger U, Šikoparija B, Hallsdottir M, Sauliene I, Bergmann KC, Pashley CH, De Weger L, Majkowska-Wojciechowska B, Rybníček O, Thibaudon M, Gehrig R, Bonini M, Yankova R, Damialis A, Vokou D, Gutiérrez Bustillo AM, Hoffmann-Sommergruber K, Van Ree R. 2014. Geographic and temporal variations in pollen exposure across Europe. Allergy: European Journal of Allergy and Clinical Immunology 69(7): 913–923. doi:10.1111/all.12419.
   Web of Science ®Google Scholar
• Smith M, Oteros J, Schmidt-Weber C, Buters JT. 2019. An abbreviated method for the quality control of pollen counters. Grana 58: 185–190.
   Web of Science ®Google Scholar
• Sofiev M. 2017. On impact of transport conditions on variability of the seasonal pollen index. Aerobiologia 33(1): 167–179. doi:10.1007/s10453-016-9459-x.
   PubMed Web of Science ®Google Scholar
• Sofiev M, Belmonte J, Gehrig R, Izquierdo R, Smith M, Dahl A, Siljamo P. 2013a. Airborne pollen transport. In: Sofiev M, Bergmann KC, eds. Allergenic pollen, 127–160. Dordrecht: Springer Science+Business Media.
   Google Scholar
• Sofiev M, Berger U, Prank M, Vira J, Arteta J, Belmonte J, Bergmann KC, Chéroux F, Elbern H, Friese E, Galan C, Gehrig R, Khvorostyanov D, Kranenburg R, Kumar U, Marécal V, Meleux F, Menut L, Pessi AM, Robertson L, Ritenberga O, Rodinkova V, Saarto A, Segers A, Severova E, Sauliene I, Siljamo P, Steensen BM, Teinemaa E, Thibaudon M, Peuch VH. 2015. MACC regional multi-model ensemble simulations of birch pollen dispersion in Europe. Atmospheric Chemistry and Physics 15(14): 8115–8130. doi:10.5194/acp-15-8115-2015.
   Web of Science ®Google Scholar
• Sofiev M, Ritenberga O, Albertini R, Arteta J, Belmonte J, Bernstein CG, Bonini M, Celenk S, Damialis A, Douros J. 2017. Multi-model ensemble simulations of olive pollen distribution in Europe in 2014: Current status and outlook. Atmospheric Chemistry and Physics 17(20): 12341–12360. doi:10.5194/acp-17-12341-2017.
   Web of Science ®Google Scholar
• Sofiev M, Siljamo P, Ranta H, Linkosalo T, Jaeger S, Rasmussen A, Rantio-Lehtimaki A, Severova E, Kukkonen J. 2013b. A numerical model of birch pollen emission and dispersion in the atmosphere. Description of the Emission Module. International Journal of Biometeorology 57(1): 45–58. doi:10.1007/s00484-012-0532-z.
   PubMed Web of Science ®Google Scholar
• Sofiev M, Siljamo P, Ranta H, Rantio-Lehtimäki A. 2006. Towards numerical forecasting of long-range air transport of birch pollen: Theoretical considerations and a feasibility study. International Journal of Biometeorology 50(6): 392–402. doi:10.1007/s00484-006-0027-x.
   PubMed Web of Science ®Google Scholar
• Sommer J, Smith M, Šikoparija B, Kasprzyk I, Myszkowska D, Grewling Ł, Skjøth CA. 2015. Risk of exposure to airborne Ambrosia pollen from local and distant sources in Europe – An example from Denmark. Annals of Agricultural and Environmental Medicine 22(4): 625–631. doi:10.5604/12321966.1185764.
   PubMed Web of Science ®Google Scholar
• Soundiran R, Radhakrishnan T, Natarajan S. 2019. Modeling of greenhouse agro-ecosystem using optimally designed bootstrapping artificial neural network. Neural Computing and Applications 31(11): 7821–7836. doi:10.1007/s00521-018-3598-7.
   Web of Science ®Google Scholar
• Stach A, Smith M, Skjoth CA, Brandt J. 2007. Examining Ambrosia pollen episodes at Poznan (Poland) using back-trajectory analysis. International Journal of Biometeorology 51(4): 275–286. doi:10.1007/s00484-006-0068-1.
   PubMed Web of Science ®Google Scholar
• Tassan-Mazzocco F, Felluga A, Verardo P. 2015. Prediction of wind-carried Gramineae and Urticaceae pollen occurrence in the Friuli Venezia Giulia region (Italy). Aerobiologia 31(4): 559–574. doi:10.1007/s10453-015-9386-2.
   Web of Science ®Google Scholar
• Thackeray SJ, Sparks TH, Frederiksen M, Burthe S, Bacon PJ, Bell JR, Botham MS, Brereton TM, Bright PW, Carvalho L, Clutton-Brock T, Dawson A, Edwards M, Elliott JM, Harrington R, Johns D, Jones ID, Jones JT, Leech DI, Roy DB, Scott WA, Smith M, Smithers RJ, Winfield IJ, Wanless S. 2010. Trophic level asynchrony in rates of phenological change for marine, freshwater and terrestrial environments. Global Change Biology 16(12): 3304–3313. doi:10.1111/j.1365-2486.2010.02165.x.
   Web of Science ®Google Scholar
• Thibaudon M, Šikoparija B, Oliver G, Smith M, Skjøth CA. 2014. Ragweed pollen source inventory for France - The second largest centre of Ambrosia in Europe. Atmospheric Environment 83: 62–71. doi:10.1016/j.atmosenv.2013.10.057.
   Web of Science ®Google Scholar
• Tormo Molina R, Maya Manzano JM, Fernández Rodríguez S, Gonzalo Garijo Á, Silva Palacios I. 2013. Influence of environmental factors on measurements with Hirst spore traps. Grana 52(1): 59–70. doi:10.1080/00173134.2012.718359.
   Web of Science ®Google Scholar
• Tormo R, Silva I, Gonzalo Á, Moreno A, Pérez R, Fernández S. 2011. Phenological records as a complement to aerobiological data. International Journal of Biometeorology 55(1): 51–65. doi:10.1007/s00484-010-0308-2.
   PubMed Web of Science ®Google Scholar
• Tseng Y-T, Kawashima S, Kobayashi S, Takeuchi S, Nakamura K. 2018. Algorithm for forecasting the total amount of airborne birch pollen from meteorological conditions of previous years. Agricultural and Forest Meteorology 249: 35–43. doi:10.1016/j.agrformet.2017.11.021.
   Web of Science ®Google Scholar
• Valencia J, Astray G, Fernández-González M, Aira M, Rodríguez-Rajo F. 2019. Assessment of neural networks and time series analysis to forecast airborne Parietaria pollen presence in the Atlantic coastal regions. International Journal of Biometeorology 63(6): 735–745. doi:10.1007/s00484-019-01688-z.
   PubMed Web of Science ®Google Scholar
• Verstraeten WW, Dujardin S, Hoebeke L, Bruffaerts N, Kouznetsov R, Dendoncker N, Hamdi R, Linard C, Hendrickx M, Sofiev M. 2019. Spatio-temporal monitoring and modelling of birch pollen levels in Belgium. Aerobiologia 35(4): 703–717. doi:10.1007/s10453-019-09607-w.
   Web of Science ®Google Scholar
• Vogel B, Vogel H, Bäumer D, Bangert M, Lundgren K, Rinke R, Stanelle T. 2009. The comprehensive model system COSMO-ART–Radiative impact of aerosol on the state of the atmosphere on the regional scale. Atmospheric Chemistry and Physics 9(22): 8661–8680. doi:10.5194/acp-9-8661-2009.
   Web of Science ®Google Scholar
• Vogel H, Pauling A, Vogel B. 2008. Numerical simulation of birch pollen dispersion with an operational weather forecast system. International Journal of Biometeorology 52(8): 805–814. doi:10.1007/s00484-008-0174-3.
   PubMed Web of Science ®Google Scholar
• Volkova O, Severova E. 2019. Poaceae pollen season and associations with meteorological parameters in Moscow, Russia, 1994–2016. Aerobiologia 35(1): 73–84. doi:10.1007/s10453-018-9540-8.
   Web of Science ®Google Scholar
• Voukantsis D, Niska H, Karatzas K, Riga M, Damialis A, Vokou D. 2010. Forecasting daily pollen concentrations using data-driven modeling methods in Thessaloniki, Greece. Atmospheric Environment 44(39): 5101–5111. doi:10.1016/j.atmosenv.2010.09.006.
   Web of Science ®Google Scholar
• Zanotti C, Rotiroti M, Sterlacchini S, Cappellini G, Fumagalli L, Stefania GA, Nannucci MS, Leoni B, Bonomi T. 2019. Choosing between linear and nonlinear models and avoiding overfitting for short and long term groundwater level forecasting in a linear system. Journal of Hydrology 578: 124015. doi:10.1016/j.jhydrol.2019.124015.
   Web of Science ®Google Scholar
• Zewdie GK, Lary DJ, Levetin E, Garuma GF. 2019a. Applying deep neural networks and ensemble machine learning methods to forecast airborne Ambrosia pollen. International Journal of Environmental Research and Public Health 16(11): 1992. doi:10.3390/ijerph16111992.
   PubMed Web of Science ®Google Scholar
• Zewdie GK, Lary DJ, Liu X, Wu D, Levetin E. 2019b. Estimating the daily pollen concentration in the atmosphere using machine learning and NEXRAD weather radar data. Environmental Monitoring and Assessment 191(7): 418. doi:10.1007/s10661-019-7542-9.
   PubMed Web of Science ®Google Scholar
• Zewdie GK, Liu X, Wu D, Lary DJ, Levetin E. 2019c. Applying machine learning to forecast daily Ambrosia pollen using environmental and NEXRAD parameters. Environmental Monitoring and Assessment 191(S2): 261. doi:10.1007/s10661-019-7428-x.
   PubMed Web of Science ®Google Scholar
• Zhang Q, Yang LT, Chen Z, Li P. 2018. A survey on deep learning for big data. Information Fusion 22: 146–157. doi:10.1016/j.inffus.2017.10.006.
   Web of Science ®Google Scholar
• Zhang R, Duhl T, Salam MT, House JM, Flagan RC, Avol EL, Gilliland FD, Guenther A, Chung SH, Lamb BK, VanReken TM. 2014. Development of a regional-scale pollen emission and transport modeling framework for investigating the impact of climate change on allergic airway disease. Biogeosciences 11(6): 1461–1478. doi:10.5194/bg-11-1461-2014.
   Web of Science ®Google Scholar
• Zhang W, Huang B. 2015. Land use optimization for a rapidly urbanizing city with regard to local climate change: Shenzhen as a case study. Journal of Urban Planning and Development 141(1): 05014007. doi:10.1061/(ASCE)UP.1943-5444.0000200.
   Web of Science ®Google Scholar
• Zhang Y, Bielory L, Cai T, Mi Z, Georgopoulos P. 2015. Predicting onset and duration of airborne allergenic pollen season in the United States. Atmospheric Environment 103: 297–306. doi:10.1016/j.atmosenv.2014.12.019.
   Web of Science ®Google Scholar
• Zhang Y, Isukapalli SS, Bielory L, Georgopoulos PG. 2013. Bayesian analysis of climate change effects on observed and projected airborne levels of birch pollen. Atmospheric Environment 68: 64–73. doi:10.1016/j.atmosenv.2012.11.028.
   Web of Science ®Google Scholar
• Zhao W, Wang J, Yu D, Zhang G. 2018. Prediction of daily pollen concentration using support vector machine and particle swarm optimization algorithm. International Journal of Performability Engineering 14: 2808–2819.
   Google Scholar
• Ziello C, Sparks TH, Estrella N, Belmonte J, Bergmann KC, Bucher E, Brighetti MA, Damialis A, Detandt M, Galan C, Gehrig R, Grewling L, Gutierrez Bustillo AM, Hallsdottir M, Kockhans-Bieda M-C, De Linares C, Myszkowska D, Paldy A, Sanchez A, Smith M, Thibaudon M, Travaglini A, Uruska A, Valencia-Barrera RM, Vokou D, Wachter R, de Weger LA, Menzel A. 2012. Changes to airborne pollen counts across Europe. PLoS ONE 7(4): e34076. doi:10.1371/journal.pone.0034076.
   PubMed Web of Science ®Google Scholar
• Zink K, Kaufmann P, Petitpierre B, Broennimann O, Guisan A, Gentilini E, Rotach MW. 2017. Numerical ragweed pollen forecasts using different source maps: A comparison for France. International Journal of Biometeorology 61(1): 23–33. doi:10.1007/s00484-016-1188-x.
   PubMed Web of Science ®Google Scholar
• Zink K, Vogel H, Vogel B, Magyar D, Kottmeier C. 2012. Modeling the dispersion of Ambrosia artemisiifolia L. pollen with the model system COSMO-ART. International Journal of Biometeorology 56(4): 669–680. doi:10.1007/s00484-011-0468-8.
   PubMed Web of Science ®Google Scholar
• Ziska L, Knowlton K, Rogers C, Dalan D, Tierney N, Elder MA, Filley W, Shropshire J, Ford LB, Hedberg C, Fleetwood P, Hovanky KT, Kavanaugh T, Fulford G, Vrtis RF, Patz JA, Portnoy J, Coates F, Bielory L, Frenz D. 2011. Recent warming by latitude associated with increased length of ragweed pollen season in central North America. Proceedings of the National Academy of Sciences of the United States of America 108(10): 4248–4251. doi:10.1073/pnas.1014107108.
   PubMed Web of Science ®Google Scholar